Face image quality recognition methods and apparatuses

ABSTRACT

Disclosed are computer-implemented methods, non-transitory computer-readable media, and systems for identity document face image quality recognition. One computer-implemented method includes pairing, for each user of a plurality of users and to form a pair of face images, an identity document (ID) face image and a live face image. For each pair of face images and based on a face similarity between the ID face image and the live face image, a similarity score for the ID face image is generated. Based on ID face images and similarity scores corresponding to the ID face images, a model for ID face image quality recognition is trained.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Singapore Patent Application No.10202007655X, filed on Aug. 11, 2020, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present specification relates broadly, but not exclusively, tomethods and apparatuses for face image quality recognition.

BACKGROUND

Electronic-Know Your Customer (eKYC) is a digital due diligence processperformed electronically by a business, which can verify theauthenticity of its clients for assessing potential risks of illegalintentions towards the business relationship. To complete an eKYCprocess, a person may need to submit an image of a government-recognizedphoto identity document (ID) to prove his/her identity. In this context,there may be a need to control the quality of the ID images foreffective face recognition.

One of the major issues in the eKYC scenarios relates to low qualityface images on the IDs. To date, a conventional approach to check thequality of face images is to treat it as a binary classification task,by training a binary classification model and outputting two classes:face images with acceptable quality and unacceptable quality. However,manual labelling involved in training the model can be extensive andquite time consuming to obtain a model of high accuracy.

SUMMARY

Described embodiments provide methods, apparatuses, devices, and systemsfor face image quality recognition of an identity document (ID). In someembodiments, for a collection of users in an electronic-Know YourCustomer (eKYC) system, a user's ID face image can be paired with theuser's live face image. A face similarity score can be generated basedon the paired images. In some embodiments, one can rank all thegenerated face similarity scores, and a ranking can represent thequality of the ID face image. Based on the rankings of the ID faceimages, a face quality recognition model can be trained.

In some implementations, the face quality recognition model can be amulticlass classification model. During the training, after ranking theID face images based on the face similarity scores, the ID face imagescan be given different labels. For example, ID face images in bottom 10percentile of the ranking are given label 0, in the next 10 percentileare given label 1, and finally ID face images in top 10 percentile ofthe ranking are given label 9 to train a 10-way classifier. In someimplementations, the face quality recognition model can also be aregression model trained based on the ID face images and theircorresponding face similarity scores. In some implementations, themulticlass classification model or the regression model for face imagequality recognition can be convolutional neural network (CNN)-based. Insome implementations, after finding the pairs of ID face image and liveface image, a no-reference image quality model and a face poseestimation model can be used to remove live faces with low image qualityand faces with non-frontal poses.

According to one embodiment, there is provided a computer-implementedmethod including: pairing, for each of a plurality of users, an ID faceimage and a live face image to form a pair of face images; generating,for each pair of face images, a similarity score for the ID face imagebased on a face similarity between the each pair of face images; andtraining, based on the ID face images and similarity scorescorresponding to the ID face images, a model for ID face image qualityrecognition. According to another embodiment, there is provided a methodfor image quality recognition, including: receiving an ID face image ofa user; inputting the ID face image to a model for ID face image qualityrecognition, wherein the model is trained according to the method in theprevious embodiment; and determining a quality of the ID face imagebased on an output of the model.

According to other embodiments, one or more of these general andspecific embodiments may be implemented using an apparatus including aplurality of modules, a system, a method, or a computer-readable media,or any combination of devices, systems, methods, and computer-readablemedia. The foregoing and other described embodiments can each,optionally, include some, none or all of the following embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments and implementations are provided by way of example only, andwill be better understood and readily apparent to one of ordinary skillin the art from the following written description, read in conjunctionwith the drawings, in which:

FIG. 1 is a flow chart illustrating an example of a computer-implementedmethod for training a face image quality recognition model, according toan embodiment.

FIG. 2 is a flow diagram illustrating an example of an implementation ofthe method in FIG. 1, according to an embodiment.

FIGS. 3A and 3B are schematics of examples of a classification networkand a regression network, respectively, according to an embodiment.

FIG. 4 is a schematic diagram of an example of modules of an apparatusfor face image quality recognition, according to an embodiment.

FIG. 5 is a block diagram of an example of a computer system suitablefor executing at least some steps of the example methods shown in FIGS.1 and 2, according to an embodiment.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendepicted to scale. For example, the dimensions of some of the elementsin the illustrations, block diagrams or flowcharts may be exaggerated inrespect to other elements to help to improve understanding of thepresent embodiments.

DETAILED DESCRIPTION

Embodiments will be described, by way of example only, with reference tothe drawings. Like reference numerals and characters in the drawingsrefer to like elements or equivalents.

Some portions of the description which follows are explicitly orimplicitly presented in terms of algorithms and functional or symbolicrepresentations of operations on data within a computer memory. Thesealgorithmic descriptions and functional or symbolic representations arethe means used by those skilled in the data processing arts to conveymost effectively the substance of their work to others skilled in theart. An algorithm is here, and generally, conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities, suchas electrical, magnetic or optical signals capable of being stored,transferred, combined, compared, and otherwise manipulated.

Unless specifically stated otherwise, and as apparent from thefollowing, it will be appreciated that throughout the presentspecification, discussions utilizing terms such as “receiving”,“generating”, “obtaining”, “determining”, “predicting”, “pairing”,“matching”, “dividing”, “classifying”, “excluding”, “removing”,“entering”, “calculating”, “setting”, “defining”, “comparing”,“processing”, “training”, “updating”, ““selecting”, “authenticating”,“providing”, “inputting”, “outputting”, or the like, refer to the actionand processes of a computer system, or similar electronic device, thatmanipulates and transforms data represented as physical quantitieswithin the computer system into other data similarly represented asphysical quantities within the computer system or other informationstorage, transmission or display devices.

The present specification also discloses apparatuses for performing theoperations of the methods. Such apparatuses may be specially constructedfor the required purposes, or may comprise a computer or other deviceselectively activated or reconfigured by a computer program stored inthe computer. The algorithms and displays presented herein are notinherently related to any particular computer or other apparatus.Various machines may be used with programs in accordance with theteachings herein. Alternatively, the construction of more specializedapparatus to perform the required method steps may be appropriate. Thestructure of a computer suitable for executing the variousmethods/processes described herein will appear from the descriptionbelow.

In addition, the present specification also implicitly discloses acomputer program, in that it would be apparent to the person skilled inthe art that the individual steps of the method described herein may beput into effect by computer code. The computer program is not intendedto be limited to any particular programming language and implementationthereof. It will be appreciated that a variety of programming languagesand coding thereof may be used to implement the teachings of thespecification contained herein. Moreover, the computer program is notintended to be limited to any particular control flow. There are manyother variants of the computer program, which can use different controlflows without departing from the scope of the specification.

Furthermore, one or more of the steps of the computer program may beperformed in parallel rather than sequentially. Such a computer programmay be stored on any computer readable medium. The computer readablemedium may include storage devices such as magnetic or optical disks,memory chips, or other storage devices suitable for interfacing with acomputer. The computer readable medium may also include a hard-wiredmedium such as exemplified in the Internet system, or wireless mediumsuch as exemplified in the GSM mobile telephone system. The computerprogram when loaded and executed on such a computer effectively resultsin an apparatus that implements the steps of the method.

The present specification may also be implemented as hardware modules.More particularly, in the hardware sense, a module is a functionalhardware unit designed for use with other components or modules. Forexample, a module may be implemented using discrete electroniccomponents, or it can form a portion of an entire electronic circuitsuch as an Application Specific Integrated Circuit (ASIC) or FieldProgrammable Gate Array (FPGA). Numerous other possibilities exist.Those skilled in the art will appreciate that the system can also beimplemented as a combination of hardware and software modules.

Face image quality recognition in the act or process of assessing thequality of an ID image can be considered as a form of fraud detection orfake identity detection, in which legitimacy of users are verified andpotential fraudsters may be detected before fraudulent acts are carriedout. Effective identity authentication can enhance data security ofsystems by permitting the authenticated users to access its protectedresources. Embodiments seek to provide methods and systems for effectiveand accurate face image quality recognition in photo IDs, therebydetecting ID images with unacceptable qualities and preventing access ofpotential fraudsters. Advantageously, financial risks such as moneylaundering and fraud can be effectively reduced or eliminated.

The techniques described in this specification produce one or moretechnical effects. A self-supervised face quality training method isprovided to recognize the quality of faces on IDs (e.g. ID cards,passports), by relating a distribution of the quality to a distributionof face similarity scores of paired ID face images and live face images.Different from a binary classification technique, which takes extensivework to label numerous ID face images with good or poor qualities, nomanual data labelling process is involved for this training method.Further, the trained face quality recognition model can be a regressionmodel or a multiclass classifier. When a new ID image is received, theID face image quality can be quantified or classified into one ofvarious levels, instead of merely “good” or “poor” quality.Advantageously, depending on the requirements in practicalimplementations, a user of the model can set and adjust the qualityselection criteria more accurately, in order to determine accept orreject certain qualities of ID face images.

FIG. 1 is a flow chart 100 illustrating an example method for training aface image quality recognition model. The trained model, which may be aregression model or a multiclass classification model, can be used forquality recognition of ID face images. In an embodiment, the trainedmodel is a multiclass classification model, and the example methodincludes the following steps:

110: pairing, for each of a plurality of users, an ID face image and alive face image to form a pair of face images;

120: generating, for each pair of face images, a similarity score forthe ID face image based on a face similarity between the each pair offace images;

130: dividing the similarity scores into N labels of ID face imagequality;

140: assigning the ID face image with a corresponding label; and

150: training a multiclass classifier of N classes based on the ID faceimages and corresponding labels of the ID face images.

At step 110, a user's ID face image can be paired with the user's liveface image to form a pair of face images of the user. An ID face imagemay refer to a face image on a photo ID. A photo ID can be in a form ofa card (such as a national identity cards or a driver license), adocument (such as a passport or a birth certificate), or the like. Alive face image may refer to a face image captured from a live person.In some implementations, a live face image can be a face image collectedduring an eKYC process or captured during biometric authentication (suchas unlocking a smartphone or accessing a mobile application). In someimplementations, the method for training a face image qualityrecognition model can be applicable to an eKYC system that has been inuse for a period of time with a certain amount of users in the system.In the example of an eKYC system, pairing a user's ID face image andcorresponding live face image can be done based on users' uniqueidentifiers in the eKYC system. Advantageously, abundant paired imagesfor all the users can be obtained automatically and efficiently. Pairingusers' ID face images and corresponding live face images can also bedone by other known techniques. The implementations are not limited. Insome implementations, one ID face image and one live face image may beassociated with a user. In some implementations, multiple live faceimages may be associated with a user, and any one of the live faceimages may be used to pair with the ID face image.

In some embodiments, the live face images may be pre-screened todetermine if they meet one or more predetermined standards, and liveface images not meeting the one or more predetermined standards can beremoved or excluded before proceeding to subsequent steps. In someimplementations, a no-reference image quality model can be used toremove faces with a low image quality. A face pose estimation model canalso be used to remove faces with a non-frontal pose. Advantageously,the pre-screening can exclude live face images that are not suitable asa comparison reference, thus improves the model accuracy.

At step 120, a similarity score can be generated for the ID face imagebased on a face similarity between the each pair of face images. A highsimilarity score may indicate that the face in the ID face image ishighly similar to the face in the live face image, which can happen whenthe quality of the ID face image is high. In some implementations, thesimilarity scores can be generated using machine learning techniques,such as by face recognition models that take in two faces and output asimilarity score between the two faces, or the like.

Following steps 110 and 120, based on the ID face images and similarityscores corresponding to the ID face images, a model for ID face imagerecognition can be trained. In some embodiments, the trained model canbe a regression model that normalizes the similarity scores to a fixedrange (e.g. 0-1). In some embodiments, the method may proceed to steps130, 140 and 150 and train a multiclass classifier.

At step 130, the generated similarity scores can be divided into Nlabels of face image quality. In some implementations, the generatedsimilarity scores may be ranked first, and the ranking of the similarityscores can be used to rank the quality of the ID face images.Subsequently, the ranked similarity scores can be divided into differentlabels, for example, the bottom 10 percentile is given label 0, the next10 percentile is given label 1, so on and so forth, and the top 10percentile is given label 9, to divide into 10 different label. Besidesnumeric labels, the labels can be ratings (e.g., verylow/low/medium/high/very high, or poor/average/good/excellent), and thenumber of labels can be user-defined (e.g., 4, 5, 10, 20, 100). Theimplementations are not limited.

At step 140, each of the ID face images can be assigned with acorresponding label. One may appreciate that the ID face images have nowbeen labelled based on the quality ranking of the ID face images, and nomanual labelling was involved throughout the process. At step 150, amulticlass classifier of N classes can be trained as the face imagequality recognition model based on the ID face images and theircorresponding labels. In some embodiments, the multiclass classifier canbe a CNN-based classifier, or the like. Alternatively, a regressionmodel can be trained after steps 110 and 120 instead of a classificationmodel. In some embodiments, the regression model can be a CNN-basedmodel.

The present specification may further provide methods, apparatuses, andsystems for using a face image quality recognition model to assess thequality of a new users' ID face image, the face image qualityrecognition model being trained by the methods described hereinabove.When a new user's ID face image is received (e.g., by uploading an imageof a photo ID), the ID face image is inputted to the model, and thequality of the ID face image can be determined. In the case of amulticlass classifier model trained according to the previous methods, aclass of the ID face image can be outputted (e.g. “Class 2”), and thequality of the ID face image can be determined based on the class (e.g.“Class 2” may be low quality in a scale of 0 to 9, 9 being top 10percentile of quality). In the case of a regression model trainedaccording to the previous methods, a score for the ID face image can beoutputted (e.g. “0.91”), and the quality of the ID face image can bedetermined based on the score (e.g. “0.91” may be high quality in anormalized range of 0-1, 1 being best quality with the highestsimilarity score).

For example implementations in an eKYC system, a user of the multiclassclassifier may be able to set quality recognition criteria, by selectingone or more classes as acceptable quality. Advantageously, if a newuser's ID face image is classified as a class not within the one or moreselected classes, the ID face image can be automatically rejected andprevented from entering the eKYC system, which improves the overallsuccess rate of the eKYC. One may also appreciate that, in a regressionmodel or a multiclass classifier, the quality recognition criteria canbe set and tuned easily and more accurately, comparing with a binaryclassifier. For example, for services with a moderate financial risklevel, the eKYC system may set the quality recognition criteria as“class 5 and above”. For services with a high financial risk level, theeKYC system may set the quality recognition criteria as “class 8 andclass 9”. The implementations are not limited.

FIG. 2 is an example flow diagram 200 illustrating an implementation ofthe method in FIG. 1. As shown in the top part of the flow diagram, IDface images and live face images of multiple users are paired, and aface similarity score is generated for each paired images. One mayappreciate that a face in a high-quality ID face image tends to be verysimilar with a face in a live face image of the same person. Forexample, by comparing ID face image 208 a with its paired live faceimage 208 b, a high similarity score is likely to be generated. Incontrast, by comparing ID face image 202 a with its paired live faceimage 202 b, a low similarity score is likely to be generated. Whensufficient data is collected for training the model, one may observe adistribution pattern for the face similarity scores. In someembodiments, the face similarity scores can be normally distributed asshown in the figure.

Next, based on the face similarity scores, the ID face images can beranked on a scale of low quality to high quality. As the face similarityscore between ID face image 202 a and paired live face image 202 b islow, the ID face image 202 a may be ranked as low quality, and viceversa for ID face image 208 a. Upon ranking, each ID face image may beassigned a quality label. For example, all the ID face images can bedivided into 6 quality labels, and ID face image 204 a is assigned tothe second quality label, as demonstrated by the one-hot representationin the figure. In different implementations, all the ID face images canbe divided into 3 or 4 quality labels instead, and ID face images 202 aand 204 a may be assigned the same quality label. The implementationsare not limited. After quality labelling of the ID face images, themethod may proceed to train a face quality recognition model, such as aclassification model as shown in FIG. 3A.

FIG. 3A is a schematic 300 of an example of a classification network.After all the ID face images are assigned with a quality label, amulticlass face image quality recognition model can be trained based onCNN. Alternatively, a regression face image quality recognition modelcan be trained based on the ID face images and their corresponding facesimilarity scores, without dividing the ID face images into differentlabels. A schematic 350 of an example of a regression network isdemonstrated in FIG. 3B. As shown in the figures, a classificationnetwork may output prediction probabilities for different labels and aregression network may output a value or a quantity. One may appreciatethat other implementations and/or combinations of models may be usedwithout departing from the scope of the specification, depending on thedesired outputs.

One may appreciate that training the model and using the model for faceimage quality recognition can be two separate processes, performed byeither the same party or different parties. Further, the ID face imagequality recognition method for can be implemented alone or incombination with other methods of identity verification and identityproofing. The implementations are not limited.

FIG. 4 is a schematic diagram of an example apparatus 400 includingmodules for face image quality recognition. The apparatus 400 at leastincludes a pairing module 410, a face comparison module 420, and atraining module 440. With reference to FIG. 1 and FIG. 2, the pairingmodule 410 can be configured to pair a user's ID face image and liveface image. The face comparison module 420 can be configured to comparethe face similarity in the paired images and generate a face similarityscore. When a classification model is trained, the apparatus mayadditionally include a labelling module 430 configured to label the IDface images based on the generated similarity scores. Further, theapparatus 400 may also include a ranking module to rank the ID faceimages by ranking the generated similarity scores. The training module440 can be configured to train a face image quality recognition model,such as a multiclass classifier based on the ID face images and theircorresponding labels, or a regression model based on the ID face imagesand their corresponding face similarity scores. The apparatus 400 mayadditionally include a no-reference image quality module and/or a facepose estimation module configured to determine the quality of the liveface images and/or the poses in the live face images. The apparatus 400may also include a receiving module configured to receive image data ofa new user's ID face image. In the case of a classification model, theapparatus 400 may also include a classification module configured topredict a class of the new user's ID face image, and an output moduleconfigured to output the predicted class or output a quality of the IDface image based on the predicted class. The apparatus 400 may furtherinclude a quality recognition module, which is configured to set qualityrecognition criteria for the ID face images and to accept or reject anID face image depending on whether the ID face image meets the qualityrecognition criteria (e.g. certain classes or above a certain score).One or more or any combination of these modules can be part of anapparatus for detecting photograph replacement in a photo ID.

The system, apparatus, module, or unit illustrated in the previousembodiments can be implemented by using a computer chip or an entity, orcan be implemented by using a product having a certain function. Atypical embodiment device is a computer (and the computer can be apersonal computer), a laptop computer, a cellular phone, a camera phone,a smartphone, a personal digital assistant, a media player, a navigationdevice, an email receiving and sending device, a game console, a tabletcomputer, a wearable device, or any combination of these devices. Themodules described as separate parts may or may not be physicallyseparate, and parts displayed as modules may or may not be physicalmodules, may be located in one position, or may be distributed on anumber of network modules. Some or all of the modules can be selectedbased on actual demands to achieve the objectives of the solutions ofthe specification. A person of ordinary skill in the art can understandand implement the embodiments of the present application withoutcreative efforts.

FIG. 5 is a block diagram of an example computer system 500 suitable forexecuting at least some steps of the example methods shown in FIGS. 1and 2. The following description of the computer system/computing device500 is provided by way of example only and is not intended to belimiting.

As shown in FIG. 5, the example computing device 500 includes aprocessor 502 for executing software routines. Although a singleprocessor is shown for the sake of clarity, the computing device 500 mayalso include a multi-processor system. The processor 502 is connected toa communication infrastructure 506 for communication with othercomponents of the computing device 500. The communication infrastructure506 may include, for example, a communications bus, cross-bar, ornetwork.

The computing device 500 further includes a main memory 504, such as arandom access memory (RAM), and a secondary memory 510. The secondarymemory 510 may include, for example, a storage drive 512, which may be ahard disk drive, a solid state drive or a hybrid drive and/or aremovable storage drive 514, which may include a magnetic tape drive, anoptical disk drive, a solid state storage drive (such as a USB flashdrive, a flash memory device, a solid state drive or a memory card), orthe like. The removable storage drive 514 reads from and/or writes to aremovable storage medium 518 in a well-known manner. The removablestorage medium 518 may include magnetic tape, optical disk, non-volatilememory storage medium, or the like, which is read by and written to byremovable storage drive 514. As will be appreciated by persons skilledin the relevant art(s), the removable storage medium 518 includes acomputer readable storage medium having stored therein computerexecutable program code instructions and/or data.

In an alternative implementation, the secondary memory 510 mayadditionally or alternatively include other similar means for allowingcomputer programs or other instructions to be loaded into the computingdevice 500. Such means can include, for example, a removable storageunit 522 and an interface 520. Examples of a removable storage unit 522and interface 520 include a program cartridge and cartridge interface(such as that found in video game console devices), a removable memorychip (such as an EPROM or PROM) and associated socket, a removable solidstate storage drive (such as a USB flash drive, a flash memory device, asolid state drive or a memory card), and other removable storage units522 and interfaces 520 which allow software and data to be transferredfrom the removable storage unit 522 to the computer system 500.

The computing device 500 also includes at least one communicationinterface 524. The communication interface 524 allows software and datato be transferred between computing device 500 and external devices viaa communication path 526. In various embodiments of the specification,the communication interface 524 permits data to be transferred betweenthe computing device 500 and a data communication network, such as apublic data or private data communication network. The communicationinterface 524 may be used to exchange data between different computingdevices 500 which such computing devices 500 form part an interconnectedcomputer network. Examples of a communication interface 524 can includea modem, a network interface (such as an Ethernet card), a communicationport (such as a serial, parallel, printer, GPIB, IEEE 1394, RJ45, USB),an antenna with associated circuitry and the like. The communicationinterface 524 may be wired or may be wireless. Software and datatransferred via the communication interface 524 are in the form ofsignals which can be electronic, electromagnetic, optical or othersignals capable of being received by communication interface 524. Thesesignals are provided to the communication interface via thecommunication path 526.

As shown in FIG. 5, the computing device 500 further includes a displayinterface 528 which performs operations for rendering images to anassociated display 530 and an audio interface 532 for performingoperations for playing audio content via associated speaker(s) 534.

As used herein, the term “computer program product” may refer, in part,to removable storage medium 518, removable storage unit 522, a hard diskinstalled in storage drive 512, or a carrier wave carrying software overcommunication path 526 (wireless link or cable) to communicationinterface 524. Computer readable storage media refers to anynon-transitory, non-volatile tangible storage medium that providesrecorded instructions and/or data to the computing device 500 forexecution and/or processing. Examples of such storage media includemagnetic tape, CD-ROM, DVD, Blu-ray™ Disc, a hard disk drive, a ROM orintegrated circuit, a solid state storage drive (such as a USB flashdrive, a flash memory device, a solid state drive or a memory card), ahybrid drive, a magneto-optical disk, or a computer readable card suchas a PCMCIA card and the like, whether or not such devices are internalor external of the computing device 500. Examples of transitory ornon-tangible computer readable transmission media that may alsoparticipate in the provision of software, application programs,instructions and/or data to the computing device 500 include radio orinfra-red transmission channels as well as a network connection toanother computer or networked device, and the Internet or Intranetsincluding e-mail transmissions and information recorded on Websites andthe like.

The computer programs (also called computer program code) are stored inmain memory 504 and/or secondary memory 510. Computer programs can alsobe received via the communication interface 524. Such computer programs,when executed, enable the computing device 500 to perform one or morefeatures of embodiments discussed herein. In various embodiments, thecomputer programs, when executed, enable the processor 607 to performfeatures of the above-described embodiments. Accordingly, such computerprograms represent controllers of the computer system 500.

Software may be stored in a computer program product and loaded into thecomputing device 500 using the removable storage drive 514, the storagedrive 512, or the interface 520. The computer program product may be anon-transitory computer readable medium. Alternatively, the computerprogram product may be downloaded to the computer system 500 over thecommunication path 526. The software, when executed by the processor502, causes the computing device 500 to perform the necessary operationsto execute the method as shown in FIGS. 1 and 2.

It is to be understood that the embodiment of FIG. 5 is presented merelyby way of example to explain the operation and structure of the system500. Therefore, in some embodiments one or more features of thecomputing device 500 may be omitted. Also, in some embodiments, one ormore features of the computing device 500 may be combined together.Additionally, in some embodiments, one or more features of the computingdevice 500 may be split into one or more component parts.

It will be appreciated that the elements illustrated in FIG. 5 functionto provide means for performing the various functions and operations ofthe system as described in the above embodiments.

It will be appreciated by a person skilled in the art that numerousvariations and/or modifications may be made to the present specificationas shown in the specific embodiments without departing from the scope ofthe specification as broadly described. The present embodiments are,therefore, to be considered in all respects to be illustrative and notrestrictive.

What is claimed is:
 1. A computer-implemented method for identitydocument face image quality recognition, comprising: pairing, for eachuser of a plurality of users and to form a pair of face images, anidentity document (ID) face image and a live face image; generating, foreach pair of face images and based on a face similarity between the IDface image and the live face image, a similarity score for the ID faceimage; and training, based on ID face images and similarity scorescorresponding to the ID face images, a model for ID face image qualityrecognition.
 2. The computer-implemented method of claim 1, whereintraining a model for ID face image quality recognition comprisestraining a multiclass classifier.
 3. The computer-implemented method ofclaim 2, wherein training a multiclass classifier comprises: dividingthe similarity scores into N labels of ID face image quality; assigningan ID face image with a corresponding label; and training a multiclassclassifier of N classes based on the ID face images and correspondinglabels of the ID face images.
 4. The computer-implemented method ofclaim 3, wherein dividing the similarity scores into N labels ispercentile-based.
 5. The computer-implemented method of claim 1, whereintraining a model for ID face image quality recognition comprisestraining a regression model.
 6. The computer-implemented method of claim1, further comprising, prior to generating the similarity score for theID face image: determining, for each of the plurality of users, if thelive face image meets one or more predetermined standards; and excludingthe live face image in response to determining that the live face imagedoes not meet the one or more predetermined standards.
 7. Thecomputer-implemented method of claim 6, wherein determining if the liveface image meets the one or more predetermined standards comprises:using a no-reference image quality model and/or a face pose estimationmodel to determine if the live face image meets the one or morepredetermined standards; and excluding the live face image in responseto determining that the live face image has a low image quality or anon-frontal pose.
 8. The computer-implemented method of claim 1, whereinthe similarity scores are normally distributed.
 9. Thecomputer-implemented method of claim 1, wherein the model is aconvolutional neural network (CNN)-based model.
 10. Thecomputer-implemented method of claim 1, wherein the live face imagecomprises a face image collected during biometric authentication orcollected during an electronic-Know Your Customer (eKYC) procedure. 11.A non-transitory computer-readable medium storing one or moreinstructions executable by a computer system to perform operations foridentity document face image quality recognition, comprising: pairing,for each user of a plurality of users and to form a pair of face images,an identity document (ID) face image and a live face image; generating,for each pair of face images and based on a face similarity between theID face image and the live face image, a similarity score for the IDface image; and training, based on ID face images and similarity scorescorresponding to the ID face images, a model for ID face image qualityrecognition.
 12. The non-transitory computer-readable medium of claim11, wherein training a model for ID face image quality recognitioncomprises training a multiclass classifier.
 13. The non-transitorycomputer-readable medium of claim 12, wherein training a multiclassclassifier comprises: dividing the similarity scores into N labels of IDface image quality; assigning an ID face image with a correspondinglabel; and training a multiclass classifier of N classes based on the IDface images and corresponding labels of the ID face images.
 14. Thenon-transitory computer-readable medium of claim 13, wherein dividingthe similarity scores into N labels is percentile-based.
 15. Thenon-transitory computer-readable medium of claim 11, wherein training amodel for ID face image quality recognition comprises training aregression model.
 16. The non-transitory computer-readable medium ofclaim 11, further comprising, prior to generating the similarity scorefor the ID face image: determining, for each of the plurality of users,if the live face image meets one or more predetermined standards; andexcluding the live face image in response to determining that the liveface image does not meet the one or more predetermined standards. 17.The non-transitory computer-readable medium of claim 16, whereindetermining if the live face image meets the one or more predeterminedstandards comprises: using a no-reference image quality model and/or aface pose estimation model to determine if the live face image meets theone or more predetermined standards; and excluding the live face imagein response to determining that the live face image has a low imagequality or a non-frontal pose.
 18. The non-transitory computer-readablemedium of claim 11, wherein the similarity scores are normallydistributed.
 19. The non-transitory computer-readable medium of claim11, wherein the model is a convolutional neural network (CNN)-basedmodel.
 20. The non-transitory computer-readable medium of claim 11,wherein the live face image comprises a face image collected duringbiometric authentication or collected during an electronic-Know YourCustomer (eKYC) procedure.
 21. A computer-implemented system foridentity document face image quality recognition, comprising: one ormore computers; and one or more computer memory devices interoperablycoupled with the one or more computers and having tangible,non-transitory, machine-readable media storing one or more instructionsthat, when executed by the one or more computers, cause the one or morecomputers to perform one or more operations comprising: pairing, foreach user of a plurality of users and to form a pair of face images, anidentity document (ID) face image and a live face image; generating, foreach pair of face images and based on a face similarity between the IDface image and the live face image, a similarity score for the ID faceimage; and training, based on ID face images and similarity scorescorresponding to the ID face images, a model for ID face image qualityrecognition.